109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
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"""
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================================================
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Semi-supervised Classification on a Text Dataset
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================================================
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In this example, semi-supervised classifiers are trained on the 20 newsgroups
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dataset (which will be automatically downloaded).
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You can adjust the number of categories by giving their names to the dataset
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loader or setting them to `None` to get all 20 of them.
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"""
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import numpy as np
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from sklearn.datasets import fetch_20newsgroups
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from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
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from sklearn.linear_model import SGDClassifier
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from sklearn.metrics import f1_score
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import FunctionTransformer
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from sklearn.semi_supervised import LabelSpreading, SelfTrainingClassifier
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# Loading dataset containing first five categories
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data = fetch_20newsgroups(
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subset="train",
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categories=[
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"alt.atheism",
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"comp.graphics",
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"comp.os.ms-windows.misc",
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"comp.sys.ibm.pc.hardware",
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"comp.sys.mac.hardware",
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],
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)
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print("%d documents" % len(data.filenames))
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print("%d categories" % len(data.target_names))
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print()
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# Parameters
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sdg_params = dict(alpha=1e-5, penalty="l2", loss="log_loss")
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vectorizer_params = dict(ngram_range=(1, 2), min_df=5, max_df=0.8)
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# Supervised Pipeline
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pipeline = Pipeline(
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[
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("vect", CountVectorizer(**vectorizer_params)),
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("tfidf", TfidfTransformer()),
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("clf", SGDClassifier(**sdg_params)),
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]
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)
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# SelfTraining Pipeline
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st_pipeline = Pipeline(
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[
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("vect", CountVectorizer(**vectorizer_params)),
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("tfidf", TfidfTransformer()),
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("clf", SelfTrainingClassifier(SGDClassifier(**sdg_params), verbose=True)),
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]
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)
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# LabelSpreading Pipeline
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ls_pipeline = Pipeline(
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[
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("vect", CountVectorizer(**vectorizer_params)),
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("tfidf", TfidfTransformer()),
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# LabelSpreading does not support dense matrices
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("toarray", FunctionTransformer(lambda x: x.toarray())),
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("clf", LabelSpreading()),
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]
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)
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def eval_and_print_metrics(clf, X_train, y_train, X_test, y_test):
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print("Number of training samples:", len(X_train))
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print("Unlabeled samples in training set:", sum(1 for x in y_train if x == -1))
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clf.fit(X_train, y_train)
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y_pred = clf.predict(X_test)
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print(
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"Micro-averaged F1 score on test set: %0.3f"
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% f1_score(y_test, y_pred, average="micro")
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)
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print("-" * 10)
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print()
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if __name__ == "__main__":
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X, y = data.data, data.target
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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print("Supervised SGDClassifier on 100% of the data:")
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eval_and_print_metrics(pipeline, X_train, y_train, X_test, y_test)
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# select a mask of 20% of the train dataset
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y_mask = np.random.rand(len(y_train)) < 0.2
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# X_20 and y_20 are the subset of the train dataset indicated by the mask
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X_20, y_20 = map(
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list, zip(*((x, y) for x, y, m in zip(X_train, y_train, y_mask) if m))
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)
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print("Supervised SGDClassifier on 20% of the training data:")
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eval_and_print_metrics(pipeline, X_20, y_20, X_test, y_test)
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# set the non-masked subset to be unlabeled
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y_train[~y_mask] = -1
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print("SelfTrainingClassifier on 20% of the training data (rest is unlabeled):")
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eval_and_print_metrics(st_pipeline, X_train, y_train, X_test, y_test)
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print("LabelSpreading on 20% of the data (rest is unlabeled):")
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eval_and_print_metrics(ls_pipeline, X_train, y_train, X_test, y_test)
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